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Related Experiment Video

Updated: Sep 11, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Document-Level Biomedical Relation Extraction via Knowledge-Enhanced Graph and Dynamic Generative Adversarial

Lishuang Li, Jing Hao, Hongbin Lu

IEEE Transactions on Computational Biology and Bioinformatics

|August 14, 2025

Summary

This summary is machine-generated.

This study introduces a new model for biomedical relation extraction, enhancing graph connections with external knowledge and dynamic networks. The KG-DGAN model achieves state-of-the-art results on benchmark datasets.

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Area of Science:

Biomedical Informatics
Natural Language Processing
Artificial Intelligence

Background:

Biomedical document-level relation extraction (RE) is crucial for knowledge discovery.
Existing graph-based RE methods have limitations in handling dynamic relationships and integrating external knowledge effectively.

Purpose of the Study:

To propose a novel model, KG-DGAN, for document-level RE that addresses limitations of current graph-based approaches.
To improve RE by explicitly enhancing graph connectivity with external knowledge and employing dynamic network properties.

Main Methods:

Constructing a knowledge-enhanced graph by integrating document information with external knowledge.
Utilizing a dynamic generative adversarial network (DGAN) to dynamically adjust node representations and edge weights.
Reducing redundant information and enhancing relevant information during graph aggregation.

Main Results:

The proposed KG-DGAN model achieved state-of-the-art performance on the CDR and CHR datasets.
Experimental results demonstrate the effectiveness of knowledge enhancement and dynamic network properties in improving RE.

Conclusions:

The KG-DGAN model represents a significant advancement in biomedical document-level relation extraction.
Explicitly integrating external knowledge to enhance graph connectivity and employing dynamic network mechanisms are key to achieving superior performance.